{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4116c770-3950-41d1-b6d0-85b58159368e",
   "metadata": {},
   "source": [
    "#How to get Fig.5"
   ]
  },
  {
   "cell_type": "raw",
   "id": "227b050b-eec1-46aa-a899-e404e51b22c3",
   "metadata": {},
   "source": [
    "This notebook contains the code to:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b422c9c-ddde-4312-ad42-d2aa62495904",
   "metadata": {},
   "source": [
    "generate the figure of the learned representations of each module from KGE-UNIT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6828ffaa-f147-4a85-a71a-7b44f4c5efaf",
   "metadata": {},
   "source": [
    "Read learned features of heterogeneous features, structural features, CNN-based encoder's output features and Task-aware attention decoder's ouput features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d14f1735-a70f-4d9b-8fc8-caf61c42887b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iNeIqK6dKXSRRf2+RAJWqPNGIeKdm/7gugucv0qM/wsDahjUptQi3/a05BtQVubllNSs/Vthb4NalwFHi7nM1iwfSSH201oeabMKr29emmgu/+b9p3eg6Is5X+EG5g9k6TTMcXU2/IZFACzUHaavIjVunprOarR6zqlcWtbLt4GqrLJtOKSOrjPKTnqTheGQbnsciVGLFZpQZVyG69rp9r7HPepXypeyzlvi3+xA49IeI1ZR0MK7QLkGNbG0dLNOZtE9QLYvkb/k7/BB/g083/FI3qOIn3B3VsCBFhLuOEBP6B1Ti9evOjdVul6BKcOHjCD0IeJjaKKwqkG2v85DwvSa78gMA+s72CbAGUEacmi7jHWCKQ3xQyQhoKpc4nLUW3th2IIkAfv55M4sxiMYEVff8cRc+/nA2hirnfKqiCEjdfctE4m+sLxfhaJjwQqsD1VZRod6tsblN8mqlJCq2yiaM1lbVWyBFGGgTqJRylknGLvC/4ic1nMw+Wm+hRjF5u5eH73lX+N5U8yISNw3tc6OaxPLtavLmqfImci0rcOggnRLUskgDIQL1WACqkFg+juc20tIuz1rgecBPbqya9fa636/sQlBS265xhlHopqqS7pR8D2RWuUpiE1/DHdXv/R5wlrMJbUtol6CutztCvo0KcyNtszTy1OnrouGqMVmy/p2+GcEbVUn1oQ91APVFBLKGL3V9XuLsLp5hYX9wrQDtEtQQ1wXOz9X6ptqGEHCGw1hjl1TgrqyVpvHGpdtXX5hwiqtk3bZygXGsA9eFv8yXrlVUhUAwHKfSGZsW88YEtfFBZtusVVXHV0xjrdgu06ae2581a0nkGVOcyjRIB6+K+saNG61tEQFcnMCsNy8sBz/UvRC0uFKKWIRp3YsUrufCBybOTOXqShHeRJ6cqz/2g331x7SVdm75iRoLuqp/dEszFhLB1lJcEqP9giprEsILXR05XSuv/Rn+Y0bzmD7yd+9ycIEjb9z8Z5bDL+y4wOPn5XyhjgscnYv4e+hD/bQEjdkLXaD9gmoIshr3VBemFY20YCtoEa1rwyNdtWLril6osYT9ikh8vZjNMl++1VdvWS9b4QPPVNbh10CyR6wO5on+Grrer84LqvCB6djcL2shZELW7wNBkH6tRkvl3muRq/FIIICTRwdAJAgywBSHo6fx1+S4+YSvK2INkzQmzq8yaU2dF1SinzApPCWEQs3xCxcwbHBjbLMY68pmufdaAD17LOuuwig/ySYUlsVi2a4tLxJ3goox1ymEAIau+uNuIKxiOvQNyEYgxtEtC9V1EZwv0ltEPaspT45SzqIo9dvm6LAv7eQU9c96HjApc3KFhEn3InQXpBvihBYrUO79ir8/LoAFHuwDX3arYrM1dEtQMwibRtTpyC7Shckkn1DIxjU1GegJI/zrcxa7HcZqzg0N9Djf/E/8Cf5J/MnP76s7gWWBOZvonKCGwiAt9ZvNUGv1VgWilkxmYCVP5RHkVmOTydmv7rrxdSYu8AADnPjTWPOZTJ9l0tLNKQymiikAYParcr9HodRO5wS1DUg7+1Qk1xYz64KdTNIvjWyFkyTbyJWNvKcGJUI+lbSqxV91+qoSbt1i/qgFUFCJ1aT8mnOkk+FzWG658oQzfMPrNSR9wAUsQ2nD8o5SJGfbNIwRVBO6qFtJDa396tgOp6ujsgUk3Mbrngf2ep7dJSlm9QqUtlwLWb/c0huPMYLaNM7em3SUeVS+oa/JZEXa7+MSL1V0Ws8R1Yc/BYaHUJZcup68qeh4qvH9ZUpVVbGLCqYQ8vOEJbES14sU10Xw/EU8huADA8fgNoeW0N081GiOZfgo+uU/PU13JXKcGhZ7MxRNFQMP+PFzdcerAxc+jrwxHj+/XPtdw0eeDk6BWL72E1076VDsyuaq7u/fHKPX296GUsV3mlSi9Raq0a6ETQ1FJFs5lelUFz7wM0m5/aA/w+F0JP+lkuWq0cDZ40TNdRUcd9WOTvYWCpGrvNU0YoG2VaCwziGNRC2tF1SjqKvZdYKmZkVtO8/AAw4nAhjWKAZZIh/ZHm/NPRXLIIg0a2Db+VV9pnVPK7WIQpNzDcMYQWUX9ea5hRJ+5IwxwsngUR8Ch/4wUxyiIhdFhTW2FHoXRa3TWE6rqcGe0FfK8T5GYoygknpJRtmBpahN8BkOvacbxSNXUUFOto0bfrM/xvFZRGh94MG//6bCGVuIgk5hjY8k6ggU1CrkrEwyhVuXAkcJk/EKP4DwnVgSfq0luJD0N93C7f6dXBf/JhfEANOtNw5lqLZuTZ56S2K0XlCtcCU0tL3ckRiHu/g1BLx4XmiYoG7qtjezYmua/Ts6uvyXwMQ+DpkwLzZF6wXVKFQ0dc7RRDrzohTpp97se3h2NkW/QjTc9YDHk+YCYZNhSbfDJgHY9LnI+qKu/cYuBt4il0XfyCSGmlHp/mkjnRJU62a2G4LUVRApBZQJxRwu/nr/HB+exX9wPfoXwCz+2jt94N/NlsdQ3bYuJlhC8oKsWVENZWSonllfq2jnSEW7mtkTka+DTgkqKYfM77mbVMW8x7rzf9LHGmE1O0rjzS46uBFYJtWTwrye198K02Q6I6iBkCeyL97GOx8BLf4ySFwOt7FMfTI1/1FmcfUlrxM+8GzY4s+OWEFnBDUv6/zJqMh2wdGuebzw9de/k3ru6tU/lr42K4c1SVqMXQzwWb48188/3/4aRTTR4LwO8n4OXYKCmkCa1rNhbLFqtvrAtqVq6Yi8ZmzXP/zlCF+cLuKlvwp8pJVGhHhjYHJezj8aeR9drCz7AoT9GLKCd7YKK7mhU4Ka7Ky0THYfNRKdTpLV8/HubNr8YrYhGbqH0Wj9z6Kdu+70gY9n4kbUfCDwB0gGO3bv/S/UkmyetMZlmROyhs5CxP2sQOGb1XpihKgnK6LWtKt1h6/402F1m+52i5lEjIx4efEOnmKBv/rnb/BvFDUK6pSgkjTLNCRJb9KoUOhyB0REWxXJaQHLfgObbxgbiQazuuAa2oDrAUdT3avQS2cEddOdO/b8KQBJ8Io0z4Uft+LCaqdgMpVad3VvlYPL2zhJ+Awb7wRlYjI9K7nWdLcfahZbtrekHhxc4Mgb42ixvMFt6v2atzdseBPddMx7r0W9AhW6CaKP8Xi9NpU9bol+OmOh5kZztLusD+zGJ+cC28Ykx6ycAfJMKdgYLNvwnsVKf8PzzmFkipYOinzeVarEVBW1WFEa21BRhgzjBdXmgV2bUN3z8VqSnHA1Sz9XqXQzgZIqnwLW4AXGOPanwCoSLruwa60UkuxeLjC+SR3ygcE4ca5LvVkjZMmb0fv46ewXsefe2/kVfnz9+5j/7RzAjpLzGC+opF4cXCwDUhW3uVlR621i9mb+DX6aLKzoz/BHs1G+80osryyk1tXpZdpnTsFrHbfvLFKZKG++f4Afnyo+j9rDkS4xwBR/OHuKOgYbRsWvqQkE20gKso51JZPpO5GzqiIQd/duakf4m+vrZWMhhZQKSoXDz7Iek7HSNdpFRhCiDOshcz11x2yaME2p0HdiHsT/1h/9qKbVQR6EnM/Vvb8McnYKWqhVkN05NTXVkPY6HclfW8bKcVzgPzyX+LPh5V+kJHj1SrblthjV3aOiVAkItalDf4ABTla+9JDYd1hjYNl4QbV5YFeT5L3Ytr1uU1CniGgniSbUF92mOnevSglJ3dvhrFLSmKiquLhNzD1tgsTfLRuiaBrGC6ptiLO7mOZtwNEiyvo8nYM9HJ2L7WkuGaLiTKeVSjkDuJjgPF0ppgLJmoP9H+Lk7NPMX8l9E/D9pVui7aJqGUzsbwCxss7+3NG9koqE1lb0Ibmglw2pe7HHRxg3vdrtCAEMXfnPwpLSkv7qCx84Hi6HINZGKKrEGEpZqJsaPLQ56pj8ezNHQHhjfPGTRJelEtxYfS5Qd5u0lTXl4KaLUpFmF/H3xgWwyCzLlOXHppB10N9AFf9i1Kccm621ibX1mW7q0nq66oLIAbf8FZjDxX/CL/Al3o89P9C0HmtucPMA6H375v+qAnkaLnQHF3gM13jfnlIMK1Yw6TteSVCtKEOrQuICdQB8ZGqLshqoq3tQ2fEpKXwfb94f46dnUwDyHYPwUfuIbAcXOEJvq3+0NK4LnJfs4VoHX3+dfu7VK/XnSQT0HACPhRk5yVm030LNMSW0CrfxOl6BsbKIvlBcgaGa3O6LJpAkXacoKSoufByhl+m+WIthXRHkRCcmB8UbUwMwT1Q1YboR135BbZCt+XF1IgSc4TB2sQp4mPpPgd4Henxc3hjBZFHYosh0XUxULWzFygKKni/0p5b53Mqmd+VGc+Mesh0KakHS2+Ax1gEjAXu6KCXGlszhYtL/Vekpo7n7zQoBDEumlEl8d7/z+SzXr4aW6tUse5JAIbosbu+8k37u3r1aT2lLkyQKqkLydnxqChf+OsoevN7FSaw3RL6GImnXwJbu/lsX5dYmRKF1+d19gQ/P8t/ZTPvcVFK2ac1GWE6bCQV1E6ZaIQ2WvMrEJkVypn0OgS2VkSC5kHu/P0oHzgQydwrKAmJdxtTrwgDaL6jbpoR2mFSe6IbBeiqo0p1J2lk/i/CClwUki5LVrLigTzrP325S+o8VzAPjqsXaL6gNUqXWPUXRruNbIuXXiVlZfYh00r0hfUDX/tjoe6Bg/HQWSj830mmsEdQvTpGqPLrTBz4OtCxHPRrHNqyxzQ+2zUrU7etLjZoRzZ27ZewcuKm0sV38GijS7awBrBFUGyiVI9eQkMqssDK/N4eLibdYbk013wSSSfshhbtZucU/N+ED07H5W3TT8zbbBgXVUgQ8TL1p7Rf0xgtSUXCiNt9hm4MnNReskHJQUEk9SKxXx/NwtJimf+5jWQyRJ9Czek0ggGdNGMeS5P+QIj0bwoqsS+xjL+EQNjGfsmlyZX1YcIOkoJpMRGAqX9Bu0tJ0UbWDVXRNubs05WUluKmS0JX4PtAzGCH+dxa5ETTBfH6TvlZlTYZ0k6pz+kFdWCOosfnubUfy5W2DL+xq1s5pC6Evd5NFFd58ZDceZhS0B2sEtbUYto2RjWUe9GelS1Lr4vbd7Amk4bgVlb7Zm1QulCovrvr72ql5QkCuAhILoKCSxvjOq1nzJ80xl8jKhPpowYoJKXcVCEQ6JdJWKKiAMT4jVQQCmAwr1tzXwD/cG91s+RuyzIPL2zgp4ive9F1QIVyG7UiIWiiopDRxv66LaJBL1h2oECvhcVCyf2jNGDWBU7VIG9J71cadAwWV1EKryjnD5i+RBjRhGlRVq9+asTUkFxTUDGyphAEg2YoOULbJiXOwJ8kmGKVep0wIFLlbsrIgghyTE2xMz6kdza4JW28qFNQNZF1omT7KEIv9r8rIuiAjAnqzbb553bID1tPq51c4lTQcn9I/U5xrazqKOm1tow0pgSEUVABwXQTnxUd1NEVRa9DBBZ54h7FZRl1lvTVHzrHY4Y0gUlgQ/k5yPMr6c1E88mad9hVtr2jSTTp0gZi0JkOgoK64dSnwGGNzAg1bCC86QPOAPaIM5dVmpHEoqB3GtpaIVf224TywXI2u15Yq0on4/hTofaBkrLgDgSPDRPTm/XHRxzlFvgAU1BU7e0CgexEFSHbbb6r7VEhev1datF3cwf/Fx/i2/Be8cXobqdAfCrTLZ0fMwhhBNSF9JOpvA5DpI3Jc4MnCRdnmIib8rVl8M79xJQA1ravvAEH+HNPCyflAtSh1JBjjYLVGzwMmEwTDpVvouGJjGVsoO/iwqxgjqLcu01sfZSN/LSdmUdlSZrha53u4Ec1cQaGa1hFDcTCl1puhIYGfV3ddVnjlwBhBBSTVJ3NgMG7Icmsy7+7yEqkxzrMZZPmeeXFruqjfvI5brIA51nRrYbGAtRglqF0gEEh1c9JFsiVilamkTZO3A5bMXTCYzXBY5qSuC5yLjd2iCouZgbX99DGX55buBRAF1LktPPulskO53vJCLZxFUHbYnhDAo4OCJ7MQIZZ5oauHM+zhI4x1r6qTGGOh2hZlV8kAUxyOnkK3v1jmqzWhCUjjFtMGq1G2lkCk3SJRdLhNXPj4COPmfdYdxxhBzbOdIjByiyilwDobb2pt27jskrjwcYTeMqWOwtoI5ggqOuy7keVeGkIslcyQiLMRxLIHNufHfnmW+aNGqCtgSdIYJahdoJGbRplUIVvSsQqSN7eYEBVQUFuI0sh2iOchmExxkuErNDItxxb3SFVcF3j+HHjUkjkiDTG/6OEpFviL/s1zVb/HFNQOE0/xcQEsUiWtxmHguBoHF/hjHODnyG6+WvsNR5YJQRqHgkrqJYcA5m1qbTI7MHBsJ90baSLfx/uKekNEoaAS60ilcvnlKupkc6+u+h52g+0HCuBu7S9g4vhtsploW8wyu4puCKqB28RaKZsID6wyDjKEQmT/WvSLCOjxqeouuUx1AJt7OO5NzfQvE1wny78V0G5BlQjpukFHSavGBvImnz/YRyEy5zYJA0pW/SkwPITqLVwhvDGCyfbJD7UIf1cCcAqpw03TbkG1FUUW9Tahu323vhSuG4vVhdJgV0sLQJIWPmBo5gTZSLsF9fLmDiQrobyaVT/FNtHiRbFikyvBFgok8+dFNvZES5vDrlDz/Lh2C2qbWA1Gyxs0qRXp2Gqx8VfWg+cU3GDWrgeJJV9kIN/OgZvaJu/mXUNYMJDYOQQi5wGIkcR2Uj6AcbGdIQW1ZlRYwYVRPDJkG8lqJBMaqoTzo5qms+XTFgd+VQ5HbLegbkl23h01swyTaMIFEQpsHmGVDQpMkrnmxMhnW4iK7vUpAMsLnAIBTIb2Tt4Nx7wA6UyNorRbUOtGCGA4hnIrcCUUsjxJYEuUODYPqcONTXwfGI/V/M01CvaO5J6vy7quwlvcTs3ZqlzuXBPhDe3Ff3+Jn33/vtJjt1tQI6kkDvINhCvKNmusi1ZwJ1B1g2pxutPFfKTMb24L7RbUBgmFVWWENitooj3wkRSBDAsuK3BTy3rOz+uxIi1zJ5D89AcLPEUP8/kcOzs7q2fHKDvNGKCgKsfI7VoTghYKLAWok7ysoerIRiioXWCVcrVGlcBmRXYLbGGTgwJVo7sctW1kvZ+Pzw2oljMACmoVavR/bRKCjak5ObfjpTDZ+sz6LEQN5+pagC8H4XfSiDJkjVBQDaTol9IoKyxqDRsmPOt8Qx8Ikx+MKJRoABXfkazv5bpoY9Vdq5N5uCs4RppkkxhPbKx1Wieh5Rt9GHSTUEry8+71lqlnOXk9XwrrcQ+Y5P+1VkFBJaQtyATx/feBXg/OsIcjLB8fYax7pa2FW/6Wkur2VJcLINzWG+pfjfr2JuYtr36++Sb11C5mtZ+2aqNmW6GgWoIxX8qm/aIroZYVThjznpSgyrSAqlxZNl7GJiioBlK0wUby9bLgwYW/9GsdTkQ9TSwiUfb1+SPBnzrEr2jjmWjNNmC3IFelzN/uuPL0qAf7wJdnwP/OEOouWasUVJJNXWlhFncmso3L2Z38KfclP+9oo3KmTZFWoPWLnLc/qgI/a6zV2hxLC5hivCQhiIEA/nqorjUd2Q4FtQVUFtMwd7TFwlTUjSJrK3inD3wcKFiMEMCjAwUH2k7SzREyqOl80fe5i9YqBTUDo5LlKxJLaLftC77ueQr71r6BHXwZf8LzsNvAzazqd7izDbRzQkG1nE1WwE2PVKTFaH8fODtTvp5rPEh18L/qr8RClD/uzdgTtEpY20wXxZeCSvTSYjeDbrooaLqhoBakEVeAgih4o+6JyCiSt8NxunP7CMvO7SqyBlrckJnYDwVVAcmZ6rp9rbnOf/cuhWkDqtoKym/ALg753rcSCmoGWdul2rvl11HCmdXST3WPVNcFnp+mh87NZoAF1TltCkQSPVBQNyETt/0fAvhUx2qkKPOTKUqdcvbexAJSAICRBygaC0OIybDbVFHOfql7BUQ1YZemoav8mOzy1C1ooSaIb/tcAIvYrG4HF/jT/njdxKKR5GWVkXBDu0Jtow3bcRf+0nqP9DhglkO7oKBWhKkpcYLL2zixZD47IaqhoJqG5WlB15JWHLa0i4sVJNByJCWgoG6hD7He7od859VMz2LqwgLxuL5MP1e0fR8hdUNBLcG3Dka6l9AdVj7fnawOViqoY1cQKXaw0WdNykFBTZDyiQq0q3bccpdClN2R7hWUJLEjaEPAjSyhoBpEGy6sHUk3Y2uFj5CCUFCJUqzNeqhrikCLdgRkOxTUbfCCMIJkS8CN01YtCLJtw7T+ECQfFFRiNiVuaMIHnvUoQqR5KKikc0h91fu78uKDBizgPFNriR1QUA3CWv+joRQZX3yZNbzgUpIAO5tVXNkWLi+BRIHEegz4tN5Tk2pQUA2hDRF+m/kt7sYaY6/f+9NTbWsi9sFuU8RuVj7W4HyBYywfzxS0CgwtQh04e2/wGK6ek5cl7NgVfYzHulfVOLRQSSuoMr54WV48xkldlVgdJMAAJ/4U6FimAgWV5KYtbomkr/r6FHibnDIQsiepVBiNaliVpUSCdgEGnb8pUVB1s/pCOgCOwqfg4Rmm9QYiTk+BRwkV6feBICh0GBODJdLgnhBAT2K2eh52JhMEsgP5U2D4QfN5ra4LZyHW3wdiD/Shksro9DdWJXi9i+Oh23nLiqiBFqpGlltoF8CGXCl/CoyfWl/5YxPRCQ1kO9zq30ALtWmi0VCVM4xIfYQDDDscvS7LANPlTC1vbJRbqC5ooWqmD4Enif6AoQ/VNBwXeHze/iqeC4wxwWfarVQrgoCuC5yLVIvLrlr5FFQDWQ9zC5n11Z/k4KBU0xdrRTWrJ4CAvN+tNwamGQ2iFY3cJu2DgkoKs7FE1rLuT9beIIiRUFBtgHmPxGDkN9gxNgZbWwoFtWmiW08BYCj0rYVsR9dsKCHgDIexXFQBD1MDfevkBgqqZuZwY005vrsv8OFZu/afgS9w0os/Z1JwxdQuX9J0pFUHLZPeP3IDBdUwXt11OSHARDi5geSAgkqqw1HJtXANSR8BYjQUVI2YutVsgiLNnwmxBQoq6RRWJMsDSxfD81MgqwsWMRKWnpJ68Lylz3GxWHZOWgBH50LeONmfQvTGOO4trVZbG62oZkey4x94y12NcTcAAoCCSuok2cWdftZauD4VqW75V85Y97I6Cbf8pDqZZZ2i6ZW0ii772G2FFiqRI5sRpKDTkoOL1Lb/AmP8F3xS6biZJP4OZ9jDRxjXc64SBAJrVwddHvZDC5V0jlTzGXgAK5CIAiioxAjueiMcTXWvgpBqUFBJfbgucH6uNRgVXN7GSaJJh5ZenVlduCbTyofeOXBTPuxdWJQi1iLoQyWEEEXQQiVyaqxddzy3ue29bAx0Et9fBts09GxlJL9d0EIl9RIKc/Shu9n0/vt6z28CWVkcnJ1VCVqohNRE3IfpAlh0dtZSV6CgklYj3VKLK/kcqZYh+9vDvNdQ4IHuDtSrA275CSEAgNfo4xgLTPCZ7qVYCy1U0j1qbhYtS1eSIXxgOmYaU5ugoBJCAABXGC3/EY7QJoWhoBLSAN/Fp/gQ/1rb+fNazaQaFFRCGuAVdqVD9wZ6loM+BJ50ITLXMAxKEdJB5nDxCc5jz7nwceSN6dOtAC1UQhSTSlcSAhgu05LWXa48T3+BA1EOLVRCCFEELVRC6qbmNK08JK3mQAATulCVQwuVEEIUQQuVAGDvzK7huMCThQuA+aYqoaCSTC78sO4bGPRnOAxGWtdDiOlwy08IIYqgoBJCiCIoqIQQogj6UAmAeFpNcHqJk0c5RocQQmJQUEkK52BPMudoVOgYMlFmYIu0HW75CSFEERRUQghRBAWV2INsUicncxKDoKASQogiGJQitaAisEWIbdBCJXrhNp60CAoqIYQoglt+YhzCB571ZD9xASzYBYsYCy1UYi90FxDDoKAS67jwgclY9yoIScMtP9FLYjxIIIBneUZz+NP14DtCTIEWKiGEKIKCSoxBNoaFEJvglp/YDefbE4OgoBJj6UPgCWiyEnvglp/YC61TYhgUVEIIUQS3/EQrskDUuhJKAKkdP61SYjAUVKIFRvRJG6GgEnNJJP0TYjr0oRJCiCIoqIQQoohcW/7Fatt1fX1d62JId3j5sgfgfuy5vYM3+Jd/+xsAAL9qpG5CPVsodCv1FjmOdnl5iYcPHyo7KSGEmMKLFy+wt7en5Fi5BPXt27f46quvcP/+ffR60s6/hBBiFYvFAi9fvsS7776LW7fUeD9zCSohhJDtMChFCCGKoKASQogiKKiEEKIICiohhCiCgkoIIYqgoBJCiCIoqIQQoggKKiGEKIKCSgghiqCgEkKIIiiohBCiCAoqIYQogoJKCCGK+P9s9AyLQu4YpQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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99jE2QVRZqCEf/Lpu87EgEc6g+OrEmKDMEG2VRCsUZO9t8SKotmvRfQhZ39U0Eksu+6DJgh8Kra+56YE/NvjsLlASSxwq4oJqG/8JBfe5JyRy6noHlPHciEdEUGN2k7ugqX5yaNc+Wvb2rGOIO/dPbvEWV7nlAiFVFuS6xMhHwkiyd0BHgsVQi3E5aVHoEwfU4FJLxzE1fCetOIkZXxriShcXu6/ZPPizGTCf9xgM8YmqpFSR5BMhDmfs2MQ3RCKMln0HFJQlaceLoBYbPpgelKhW0wwcVgFYcGAI1B8deR8GKRB7+76qNnNd2vnxId7g26r63Tnwy+fbr/3JI3/nJxGgcfeFgLFUEUHts22wj21HhkIM1893FYjzpcY+SqRS2Asqhe9ggdoYah9M/UJ9CLeUJR10X/sYEYrtid0Xthn9gYiMCFLx26JF/W//JjW6O6IV1BiXhbpswbaPDKfLitIa5TzEDc7wo9KrMwALZ+ccVCxdSozauNJV7+l6T9pWTrRg9P79pkPv/v79iz2fGefdprRsxTF4hB6q0YMBCVFXYmwskiIBYqlBLNSqhFXdFh0h1tlrS4bVWlUZxNd1/8UziqcICrY3FqVvqMJl+VXIZb8IJKj5uv2yOKVYG1iuavBOi9nZxwRinBSyD86afDh37SPrhuQFxc1Liox+//vdF3uGEcQEVWPjZlcCUXdc3zsW3J9jCuR7NmGBU7xwe2JNMLkTD8tlN7HtU9tblST81a/aj6MBpxZqUwepcua9SXybsvc5fUTTtQh6CRnMToDF7YVSsLVuI4oF0YfV7vwcGq5vvoS2q+Xq4Dusv/hi98WeCzA6Car2jlK2SH6PorCr3w44kridthh2JX0e9psb4HCKTRBcAK3LQ7tappGhqmxKKt4lHbc0xXoJ2aJOyOoEN8uAwxOXI5Pdf0lasF11nAqEKkHVgCvLsq9l1TlG3eIGG1Rtpk+0WY09Ey87aFx+asF6MsEIwNXVFR4/fixyTDFBLW/R0DYWWVcy1QcXLqIpliuN7XGrtu5u2y+BxEVlOGQudIKqSUCilWCd8GqbfFrizEK1FZ38YW8rTCZrqukYbc5BESKAH6t9jPc4g5BLPnQCC3InQa0SM2kLs7w9c9dj1MVT67pi2ZybbnJHLG58p9dW+sFruzSyGNeUfODLIZ7zc+D588q3B0e6ZtXBEtU29LZQbaw+FzuKVrk8XcIOpo37QqF6cUPk7phTtPZAlR5X3XLOm5vt5NcAYVIK5o37imgs3wl9ftKCotXY0SLrPNG6ynqbjlu17POt5hrCTZMU/OVfbr/Y0VBwKqimRFMVbKRSTZu61qhDEKk1FXGc/U52Uo3Y63ng+4S5W57/+b4h8vN/+W7z302ruUgLzs83AlL8G49Dj6oVeS6g+Dc/cXzS3FUu/p1YnLTr5wKTLR1eU1OI4+rK2/URWylVRDJz34c+x0t29tdCVVMRX1Zq5d7uPY9ncnvLCSgbVzkVCu5+hhne3Pa4neQvmu6DNvdAXVzflJBznKASc/nLIuq6w5Ip0dW36XTXz9lMHDFtY11JgFUo4vHr0Im11MIaRQRixbGTZFKqy0aAdTSJXN8qAY1JL6KLuraHK0zwOjetlwB6GBWdiVhA15OJmKHgXVAlakv7oLosaWh07SW6XACjF3KWZdElP/45gG8q39p8/0wxma0393eFyHy6Ab4elT6DX+tquejDG4lYhKsI1mBa006nxXCFi+Wvg+HZM78hAQdZ9PHbf8YZ/tl/prnYctGWu7gtnDXolmQ6A84WhRcyRyfKwyqmZbKOa4PFVkppphhbDdnwmhjQ0KvTFSG7+edWPCAzORisyU/HM3z9dnPcfQAvy5/pct62n6m7xgHuLXELte2qqL5NUTTUr5oWBtCyleFu8pZyD30nTgwP9fcZxCxKJyGs0nVZYYI5FruCSXZIMikF+G3y3Ndij83ij52Ut43ZR4aXAfz/K0zxSnBb9FjxKqgaOtknI16hy398jyPAlsAmJO6fZO7BviQY7knWQu0Cb/QIcPUQdkkKhWJrUpnAXXYnEhQJs5d+qDk+S5bKDa8JGQJXmGKel2AJegq5S59zt8uvyNHTwfk20k3xlId7u0mltrWqtCzJULlrTi3Z1alk8Y1B4bRFrcsvvdppiDhfgRVpYfZ4Cpy9y0rrxwGcxNvlqLWYuo59dz2+ltxAR8S6TZmWX364aP4cuz11JLciin+LxeaGvO2sMz4c4Qwj/AIn/sYV0c2fLHkDFm2/Q74QI6LOWG3xaqH62NyOCBC5ldAXJ5Z9wzVtdU42IVGLU0H9w5X7rlNEGD6g/anqXJ8yvG8ACApq0zYiNnTtozpkytfKuASQEOIFlUkp11n7ri5drG32dptSZDIuvaL6v9a4HLtp583f/tbNubp+Dx+/XfGesrVgY76nICioZRGUbLpM7LnCFPO8fVwkrM4v8fr5tosz2b/A6eoozIC0IN2msA3ScfTIhdIWVRZqrBZgEtS1PBtQQgpw5CE9eIDVu/X2/b0EJieb+1u8CYxGBpDs9LpSqohpq5QvEo/bAxFMGgOxJAC4e8BNG8U57sPphWIjbmD7Wg3pvqlBlYXqo3FKn4RX11pbl0hbU+oFf4iUN/mLwaIbYqUDlAlqHXVC6OuBN1UyPDm6//82Yq3CCvfU+Hiw4iy9o2ospByusCQaQfWBegFwHYNaLm9XV21v+eua8bMDg5V9JHNw01bC+/vAatXvuK5+iw5F+07u2/KkUDcW10IaUew1mKDa7BSqSsyUsrvhm+V167Papur9Sm9yLQy+ic/enlycVel23M4FtWr2dLW3k3Yrc1BtBS8vAZTiJBcXELM++9InkWKqNb246DMaOxxsTGgFk05WqHL5fQhfnz2oorIwbNykGst0ZzFAili6kqZJ+idHf46fuRwbUL9LQYutr4MyMK9FlaDWISlmVcdaZf2PaxLrqolCuzXdl/HBp02vziJHM8BTbDYmkrsXBiakOWoFNcQN5soCrdrZtXXYw3IHzR/fZN0TCD0fhO3fbQpgHXYDvGfP3Liqn322+1pdrWljnLr7VibjvQ/1963rpI7LcEBklQPBBDUq99mA79iwkSzD+PBwu5v6bLZZ7eTyHqx7gDKH5yVy1BXp57QRYp+ZeMXWr3NBtRVOqQ5TLoXaRxesttu/OKPFA9J4XWLaAM+S7/em/pI02vufahxTINS6/LESenPCSnIxzLLqfxsalq6qKm+K2XbViAuqbexThcgEpk/FAUmLMd7jbHYyzIktIbxaqH1ENOaMp4iF02ZtdJ0V48nCifn3ckLVct+7mPehuzX7NquetBTKR7QqygRdfgHyZFS5e1ZZVEwTSq+1/aWVJ3fHrxkDgb+HNlBssdFLtO3l0GfyjUgEJVElqNoffhcuutqVUy0eJlUxRuIfKa8ngeSWKkGto65MKamCaAIgwUL3nKryJJIED6QOtMo27m6V9WYTP81LhmLj/ZKJJWk09p7VTtMz2Iss28R3i38nJw5OFDdBLdQvjv00la6jrSXUZe8sERwmk2KyBmMaqzUhSqECnHN3h94ML5tWoEQWi43G5Y+Jqgc8WKwxcOZUnQj6EhMFNaPRTzZdCHi/qxZU1qqmy/tlfUVEssRYFhQiWaT9mlSgWlBNmB48ZpjTo2m7GTXEKJApE7hSQExQbdxZKeHT5EKyZMgNrlosesfjA266ZtfnGTDaPv+H/RmerBb9TlZcyswJ5Y6gFqoGYXQhiBq+VxtsroGW78QJLF52f7spgIoG2pESncvvA5vYbVsxcRYzLNyMY2DTym/HQpj6T44YxrU6/jlev1XaWb4Ira608PjbeRPULssuk3H7DHhp01chDKv5QoW1GfK8wWl6yDWJuqNqhXtNmALYHD/2+4EWakdyizPEDaCuK74ljZ3lY8SF2Ph0gTUJtwSBS9WcCWrXkqeq7UKKoqUtjha6vGsf2baY5p2D5vPdNy+X+PHfn4D7OnnCthGJIx4/m+4IzBPJE9Tu3pC1F+ubG2D0o3afUURQC9XUGCS0OElRnATqvtPq/BKvn2/XCE32L3C6OnI5PGv6JKNWmOB1cU+UJTA5idulE6VKcEwTIYkCuvwG6mK3XbefTq2RtF2LuMzt3laAG5fV1m2sO3eT275cJpPZDoqykIVXQdUacO5bEmTTyKNSpM/tztGJCnfTtmVgKt5Ca1w9pMVm0l0+G4nba4vxmcg+uJ+EHUILtQVVovg7l6JoM44MUd+EUeEjYeQzseI7iWOz22rEOBPUromjqk3umhJVIQm+TFLBlicxYfRIIqiSUEmdNZ/fe20mIdMOr662hnHA4C1UV25tsZC/th3gswPDxHMkPyAh8u9iUwusrRpDLV1F4vwceP58+7X9fWC1khgV6YBeQTWstMkwwxuW++wQYlmoGrH0ZYHf3Gy7qkVMglhlaeWWmhZLS1lSpxUKxy4mqFUrocoNpNW46rc/xhi3yzVxL9htx1gUl5QSOTGt8RcfS/nBTGS9+W6TZ+BloLHUEun1DmKhanoIu1K3x5VKFM7mtYQebx63qztn1f5Qttt9p8ClocTl4mL7vwcUx/cuqFX7L9msbZ/OgLOaf5disM2PB4LZ8j4BMo8rmlLJdh8YMrJHR27OFcE10htDDcB0Bnw53xV803LYqsYuamKLt+SWdF/XzrtFbmP5DJ1nz4J2ESO76BVURW6Ci40EJcMe2kQ8FnZ/gykm+LW5fKrP/bi3t/1ZRaJ0hSnms3WcHpgijcjx0rFfZaKm6sfIfA9km9jiy5rEXNNYvGMZc+58jVxMAjY1rJERzEI1VQD07RHqU4xsl29q4QpTvOrQc9JmMnRy3X3G5orMToBFjx82UiHoRQSxTV94EVTpZiMuSbUEqg5vE1HozH0HWl0b2+83RNEdCHpjqFIsF8DJV50eWlPTk8/2gX9cbb+m2U3v7OLd9aWcoBwHafpu+8hwujwEiom8tm3pKDr6JyBNY1GCnKDe/vg7PTBh3xs0NDZjk1qjn/L2LrEw1Jir6T7/yXGGn71VLN6RkL6FGhnlhzy/+bXVxbadGD/dAP/vUOmqHJd4qDfd/BZT5PsyARX3yJ3Fu+t1NOLCY0jQC1EhqFICMZ4CZ+8yQzZy1v/gFWi2uCUY4z3Oct+9IAYmS/rud8zAdoKuyLe3Sc1y1B7esKSXoJo2i/sC/9FzSOmgObZaxFej32KlAaDzWpRpFRYoW1yK6k1FSUT8XNBZUNtYZsUdQk0ra0SXepZu6rtxdjx+sV3d68Pdtnym71PX4s62rV9w+rhjhs9+n4FWa1sCb/AnioAIx2CgiLv8D2c/xdmiWnCrYoQaGEqSIoYbU5ohfucqzGGcKYox2Lteq8X4L3utNqIihiqJJoGWQKvItx2X1PeIShg9JV2sr22CSSBtqBXUWB4cn4Ln+5rE8hsEwVcccSgiaLP1yXwRaHD2iAqq1MNW1eLPF1XNsrt+No+1pmQ5h8Jmh1nimKGIfAc6C2qTZWZruflc6tlH8B/uAV++2x5fm94DWl13QsSpst57inAMz5Bal99EsVpAq9vp24LqUiHR58aM4TcwYfudGea4pdxrNRfJFJpiOyQqQbUhf3B8JafKZVB/83/cnzNVbMSsacvu6ASRNZ1J4V1Q297wpjZ/Wnm/BH75vPl9ZWyvie+SM9+TU1QwjuiOiK+tKgvV5JZ1fZj7uLXSjUukGqpoEjbTtjA+LMEY4mhkuKgSVG1oEjASluhCCSGp3A0jSz68oV5QY7ZIfDxwoazCVCeb5Noq2oiYzXtcueF5s5f5PAmxdS6oqT54OeW1/r7RtOV13y1sJCbP6CZgV0JV15hlp2B+Ln/+gRLEQpV66F24YS5itk3jjEIESg/oGMDf4ef4V3wTakSkDtM23C6x6ax1bLk6JmKcCmpI6zTGmFdb4RW5vj3iWgfHAJRWYMT4+5P4UR9D1UBMD2KshffSSC+wsPIiYky63NzUWpaiE9PeXjrtCCsILqg+LYnYxSb1eLR32ghgjGJZ5tEj4OPH5ve5/F51MePy61m2vTILUH/NvQtqrGKmlZCNV8Z7H1rFfqsmhGTuCVPc8uLC+zAAAAeGJWV/9Ve7YtRhVwEnTdJN46iKuSqe3IJbqH1wkcyJ5ZjeiXj1SmvaPNxa8f17Den+qMGJoGpICGhZNtnmWtjseCpOog9C05p/EgFvlWY8a4jaQnVB28mg6+QhOemIWcAVrtRqvqiclLS66168Apt6zqMjx4PoScOEOkaGs9vNwFaY4HXb7acHRnBBdX3jJ+FuF9AqYFFSt0SyzN7ebkd5HxjOtTr+OV6/3a7/Tf6+KMZITb+PEoILahlfxfox34Cxfh+RyU1xQqJyG+kuPUQVbkF9t7mf661emr674jDVg9AD6MrvzjexxeLf/x2HHpUs3O4jEPn2zUXyNeepkpcoFf9S/r6OcGKhxuxm+yoIt2m2MdTESl0icXL8BKc+B1OkGDMl3QjZiMUD6lz+0LQVsa6Th8pJp+JGHiNxV70LMY/dluVy+zcL8Z0ju28oqD2RjmeGLvOKha2M8xJAZCvgau+bqhjirZCMAZy5HqA0Nt2vFAulLeoElYX1zXT9Pm3EX23iK8+2ZwC0TzwRu66kG+oE1Za/eOZPJEP3HPV1vibruG+/U+csF8Doxeb/97V22mSdfVGO4UpadFUVCnVIu+MJWKrRCqorfG+EV4Tu/oYuFvg+Mpzixf0LeVZe4sE0iWu5qD9SASCypCOokQWvu6LRFbcak2L3V03TlqKF3Ze+z0PX30vaylR835hIR1AD4To+a/NQ+2xLqMXt37ruWlzyFjSO//g4yrXslbTZuC9iKKgN1NWlarQW6/CVnHuKc5wunwPFks39fWC1cn9yV/h+8ItLXavOf3MTXb/Q1KGgVlAXz7Qprq8T26p/+9t521HKEu3upvnKppCWjuuQk8nCM5wzWwJvRhULIFy5430mm8hc+iaiXXqaIrmgFf9aWbvLxXCXC+YPZvFPQjTyJZkmwch7pJYL4JuOVV7eqakxdREuR21NOhZqYjNdFVr6vBbZsWzPATwPNJgO1IZCMg8DMHXX79v2765eN3NvuQ/k2bMhHUENQMjmJeMpcPYuMzwsM/nz8Fnpj6Lky+rmCV6PNqVmL9WvjogLCmoLygmnoTYvaeTZM30Wi2SMM/+chEjahgtM3FqGmryVoUNBraCvZVb3eVp9ARCIU971D7jtHbAP4GX5TQGy7LX3kzGZBf3LdiOFghozjF25Z2uVFLaE6ApTzGdruTI5jSVPGsekGAoqIRXQlSZtoaD2gK47AdCtaXLAJNX9fTsFUNMAhtZpayioHbBZIRXbKqrkcVGa1Adt4Rpt44kUCqoSKMCOERIM42+S9T6sW2h9eoOCGhEU3cigkA0OCqoQWrowETmcxcjrhJYiHDVcy6+YXKSJEG3Xptu+39RHYD6XSzrlXaWaxsG198GhhdqB8RT48l19SY2UdfPpZnv7lbaoCxOkYIEZvsOn4xm+fru4+29j0X8I+qzEIq2hhaqEXKQHSb5dCXHHbCbfiYvsQAs1QoaUiFJnYfehGCclSUJB7QiL+sMRehfa3piamtz2B5jMpjgtbwiY0lYoiUNBVUTfjvmm935xLDCwlni3KpcL4OSrZje2oRZ1d9xTTExr9SssTGM7POn4cN3xJDrok15QUJXT1xL+/a1xo8aK07BdSZk7AZrAqkrf8B0evl3i5fHfA66NSdM+UkVywa2aOFJICiqGSamhsFzoKaNxtV0Jud83SstvPTBooSojqSSMAGULffAdoBhPVQ0FlUSFr61fnFByt8cAzgrudj5Z3CfcbmO480xXiIRUQkFNCC0b+DmvgHDUGWmM9zjDrZoNKa4ovb30gKGgJsidoDHba0cXgTZuLZKFu97Hx3bhAFYCOIWCmjLscdmJxjh2VaZ8Pnc+tkrybaNJUCioykh9wQCTbh2w7fbPkqjgUFAHAoVMAQ0eQ+qT6RBgHeqA8d4eMMuAw6nHE3pGsr7WdSs+1gI7gRaqUmhRVmDh1iZ/7aosXTZdCQ4FlainKJA++ow2ut4GQVtlwOvSilAvIs5svSooqMQrW7WeQNpJk5sbYPSj7dd8fN+Ur6lyGEMdCINuYE2IJ2ihDojgWWTWxdrDaxUlFFSlBBc/13SsmbzCFK9QujBLYHJyH6+UvHZRJrjypaQ5DAF4g4KqHRZrt+s4Feh6GUU8+4Byv2lxbIv+iRcoqEqoFokpcGuRTbDAKV74GxTpB932wcGkVIRkt3sqvRp5LswnccJdZb1BQSV64INPIocuPwlDj72livHKPFSS74RqLPzv0e8zuuSgxj27BgQt1Ij4dHyCV1jjDRahh6KWK0wxn603ohIzrtfyEyfQQlWCjSUUuhM/cUxTht604+nAKj60Q0GNiOjcTxIGVhcEg4JKwjGdYvVuvW11l4r0CYkJCiqJmmqrfUorjXiHSSlCNDOb3TeA/uab0KMhDdBCJUQLTbFPxkbVQwuVEEKEoIVKgjK4ygU2u0kaWqiEECIEBZUQQoSgoBJCiBBWMdT1bWbx+vra6WAISZ3Rx494VHrt0x//iP/is+WdXM/WgpUTo7XF0S4vL/H06VOxkxJCiBa+/fZbHBwciBzLSlB/+OEHfPfdd3j06BFG5eYMhBASIev1Gh8/fsTnn3+OBw9kop9WgkoIIaQZJqUIIUQICiohhAhBQSWEECEoqIQQIgQFlRBChKCgEkKIEBRUQggRgoJKCCFCUFAJIUQICiohhAhBQSWEECEoqIQQIgQFlRBChPhvCBxtnMKq8JIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UZBU70hNlLOUEKuXLfHIzW0/pBCrFsy2WWtQZqLvcfG/EWW8VFfSlzkClWUVc1g+2VP4s/6yiPDnJfSTts6QMdlJ+oJam59CZndxmj5OTr+2XkX8XT19cz9xrn1CSuof8sw/zTOvLqXLMsu/Srw7iKlTUop5AvT2ZVMH9gKx/hL7UE6jruLq6WbGmtLyysmGgSE5C1EwPla9MAMJO2qpQx+DqTV/5XcAN9QZqj/dZHyvABCVspf0h/1jLenIvp8r9+kDFFeoim6wEUIUBwdqvUAFGIlABgghUgCBt9VBvG7NPmrsnm/v1ARXqWsygA2sQqABBBCpAkP4CNWj4fnqc0qtJSkebfynQqP4CFWAgbc/yL3J2dve5H39c/TW/zKB3d/dNlziEjahQx2S1ADRNoAIE6W/If3Bw97lnz9b6UleTB1ZRoQIE6a9C7XWL5jYTTL3+rmBL/QVqTgIKmmbIDxBEoAIEEagAQfRQe6F/C4NToQIEEagAQQRqbVwPAIolUAGCCFSAIAK1BcfHhv9QAIEKEESg1ma2nnQ6TenkJPfRAHMEKkCQtXZKTX/ZYXNxcTHowbCZyeVlenjruY+fPqWf/Z3gXrM8mwbuIJxM1/huZ2dn6cmTJ2EvClCKd+/epYNFd/LYwlqB+vnz5/T+/fv08OHDNJlMQl4YIKfpdJouLy/T48eP04MHMd3PtQIVgPuZlAIIIlABgghUgCACFSCIQAUIIlABgghUgCACFSCIQAUIIlABgghUgCACFSCIQAUI8v933cnEDJ4ecAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "SRC_PATH= \"../res/csv/\"\n",
    "###ReadFeatures\n",
    "ddi_hf = np.array(pd.read_csv(os.path.join(SRC_PATH, 'ddi_hf.csv'), sep=',',header=None))\n",
    "dti_hf = np.array(pd.read_csv(os.path.join(SRC_PATH, 'dti_hf.csv'), sep=',',header=None))\n",
    "ddi_sf =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'ddi_sf.csv'), sep=',',header=None))\n",
    "dti_sf =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'dti_sf.csv'), sep=',',header=None))\n",
    "ddi_encoder =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'ddi_encoder.csv'), sep=',',header=None))\n",
    "dti_encoder =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'dti_encoder.csv'), sep=',',header=None))\n",
    "ddi_decoder =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'ddi.csv'), sep=',',header=None))\n",
    "dti_decoder =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'dti.csv'), sep=',',header=None))\n",
    "\n",
    "y_label_ddi =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'label_ddi.csv'), sep=',',header=None)).astype(int)\n",
    "y_label_dti =  np.array(pd.read_csv(os.path.join(SRC_PATH, 'label_dti.csv'), sep=',',header=None)).astype(int)\n",
    "\n",
    "y_label_ddi = y_label_ddi.squeeze()\n",
    "y_label_dti = y_label_dti.squeeze()\n",
    "##draw figures\n",
    "def plot_embedding(data,label,title):\n",
    "    x_min ,x_max = np.min(data,0),np.max(data,0)\n",
    "    data = (data-x_min)/(x_max-x_min)\n",
    "    fig = plt.figure(figsize=(4,4))\n",
    "    colors = plt.cm.rainbow(np.linspace(0,1,2))\n",
    "    for i in range(data.shape[0]):\n",
    "        plt.text(data[i,0],data[i,1],'.',color= colors[label[i]],fontdict={'weight':'bold','size':20})\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.savefig(title,format='svg')\n",
    "    return fig\n",
    "\n",
    "##TSNE\n",
    "tsne = TSNE(n_components=2)\n",
    "\n",
    "x_hf_ddi = tsne.fit_transform(ddi_hf)\n",
    "plot_embedding(x_hf_ddi,y_label_ddi,'test_ddi_hf.svg')\n",
    "\n",
    "x_hf_dti = tsne.fit_transform(dti_hf)\n",
    "plot_embedding(x_hf_dti,y_label_dti,'test_dti_hf.svg')\n",
    "x_sf_ddi = tsne.fit_transform(ddi_sf)\n",
    "plot_embedding(x_sf_ddi,y_label_ddi,'test_ddi_sf.svg') \n",
    "x_sf_dti = tsne.fit_transform(dti_sf)\n",
    "plot_embedding(x_sf_dti,y_label_dti,'test_dti_sf.svg')    \n",
    "x_ddi_b = tsne.fit_transform(ddi_encoder)\n",
    "plot_embedding(x_ddi_b,y_label_ddi,'test_ddi_encoder.svg')    \n",
    "\n",
    "x_dti_b = tsne.fit_transform(dti_encoder)\n",
    "plot_embedding(x_dti_b[:286],y_label_dti,'test_dti_encoder.svg')\n",
    "\n",
    "x_ddi = tsne.fit_transform(ddi_decoder)\n",
    "plot_embedding(x_ddi,y_label_ddi,'test_ddi.svg')    \n",
    "\n",
    "x_dti = tsne.fit_transform(dti_decoder)\n",
    "plot_embedding(x_dti[:286],y_label_dti,'test_dti.svg') "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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